Regression-Based Hand Pose Estimation from Multiple Cameras

de Campos, Teófilo Emídio; Murray, David William

doi:10.1109/CVPR.2006.252

Regression-Based Hand Pose Estimation from Multiple Cameras

Teófilo Emídio de Campos, David William Murray

CVPR 2006 pp. 782-789

doi:10.1109/CVPR.2006.252 /cvpr/2006/decampos2006cvpr-regression/

Abstract

The RVM-based learning method for whole body pose estimation proposed by Agarwal and Triggs is adapted to hand pose recovery. To help overcome the difficulties presented by the greater degree of self-occlusion and the wider range of poses exhibited in hand imagery, the adaptation proposes a method for combining multiple views. Comparisons of performance using single versus multiple views are reported for both synthesized and real imagery, and the effects of the number of image measurements and the number of training samples on performance are explored.1 © 2006 IEEE.

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Text

de Campos and Murray. "Regression-Based Hand Pose Estimation from Multiple Cameras." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006. doi:10.1109/CVPR.2006.252

Markdown

[de Campos and Murray. "Regression-Based Hand Pose Estimation from Multiple Cameras." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2006.](https://mlanthology.org/cvpr/2006/decampos2006cvpr-regression/) doi:10.1109/CVPR.2006.252

BibTeX

@inproceedings{decampos2006cvpr-regression,
  title     = {{Regression-Based Hand Pose Estimation from Multiple Cameras}},
  author    = {de Campos, Teófilo Emídio and Murray, David William},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2006},
  pages     = {782-789},
  doi       = {10.1109/CVPR.2006.252},
  url       = {https://mlanthology.org/cvpr/2006/decampos2006cvpr-regression/}
}